Improving Golf Course Throughput by Modeling the Impact of Restricting Early Tee Times to Faster Golfers

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ABSTRACT

A model-based decision support system (DSS) for operating and designing golf course systems is presented in this paper. The DSS is based on a simulation model that accurately represents the variability and interactions that impact pace of play on a golf course. Research shows the economic benefits of understanding the impact of policy and design on golf course play, specifically throughput (rounds played) and cycle time (round length). A specific policy, only allowing fast golfers to begin early in the day, was shown to improve both throughput and cycle time. A new statistic is proposed, the time handicap, which measures both a golfer and course's pace of play. The DSS model was developed using MS-Excel and @RISK, a Monte Carlo simulation package. Using MS-Excel offers a much greater degree of transferability and usability than traditional standalone discrete-event simulation software.

INTRODUCTION

The golf industry is big business. In 1999, golfers spent $16.3 billion on green fees (National Golf Foundation website, 2003). Because of the Tiger Woods effect on popularity, the number of golfers is increasing, creating the need for more courses. Over 400 new courses are being constructed per year (National Golf Foundation website, 2003). Golf courses, like most business operations, are designed and operated to be profitable. Many factors influence profitability. This paper focuses on improving profits by increasing throughput (the number of golfers playing golf per day) of a golf course. The approach used in this paper is unique in that we apply proven math-based modeling technology to model golf course daily throughput.

Daily golf course play is a stochastic system where random events (lost balls, weather, and poor shots) and interactions (waiting for the group in front of you) heavily impact the pace of play. Although complex, daily golf course operations are very similar to other complex systems such as:

   A manufacturing plant where parts are moving from production
   process to process A distribution network where transportation
   devices (trucks, boats, planes ...) move from location to location
   An emergency room at a hospital where patients wait for treatment

In all of these examples, performance is impacted by variability and interactions. However, these examples and other complex systems have been analyzed with math modeling. Therefore, a golf course system should be a candidate for math-based analysis.

A model-driven DSS that represents daily play at a golf course is beneficial for both designers and course managers. In both, qualitative measures and experience are the primary tools. Little empirical knowledge exists that provides the quantitative impact on throughput. For example, how much do the following impact throughput: fairway width, length of course, elevation changes, bunkers, green size ...? Certainly, designers know that the above impact pace of play. However, quantifying the impact is much more difficult. One study has shown that the number of bunkers and the hilliness of the course did not influence revenues (Schmanske, 1999). Similarly, course managers have questions regarding throughput. How much do the following impact throughput: tee-time intervals, shotgun starts, group size (2, 3, 4, 5...), carts vs. walking ...? Increasing throughput and controlling, or at least forecasting, cycle time are two of the most important factors in revenue management (Kimes, 2000).

Before proceeding, the first question to be addressed is 'Is there potential for math modeling to increase profits by improving throughput?' Consider a typical course with a $35 green fee, 12 hours of daily playing time, an average round taking 4.5 hours for a group of 4 golfers, and 144 busy golf days (weekends and holidays). Based on a 4.5 hour (270 minutes) round of golf, a group (on average) finishes a hole every 15 minutes. In a 12-hour day, 31 groups (124 golfers) complete a round of golf. …